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To slow down convergence and to favor the apparition of eco- logical niches, we use a steady-state evolutionary algorithm that involves a population of N randomly generated well-formed programs distributed over a circle and whose mode of opera- tion is outlined in Figure 3.

Figure 3 To slow down convergence and to favor the apparition of eco- logical niches, we use a steady-state evolutionary algorithm that involves a population of N randomly generated well-formed programs distributed over a circle and whose mode of opera- tion is outlined in Figure 3.

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In this paper tion (DIVIDE) makes it possible for a mother cel daughter cell. A direction parameter (cv) and a dis ter (7) associated with that instruction specify the daughter cell to and one new afi current one is t we use a small set of general, low level, de- velopmental instructions (Table I). A cellular division instruc- to generate a tance parame- position of the be created in the coordinates of the local frame attached to the mother cell. Then, the local frame associated with the daughter cell is centered on that cell and is oriented the same way as the mother cell’s frame (Figure 1). Two instruc- tions (GROW and DRAW) respectively create one new efferent ferent connection. The cell to be linked to t by the instruction parameters (Figure 1). However, no conne tion is created i synaptic weight ify the values o instruction DIE f the target is outside the substrate’s limits. T f a cell’s time constant 7 and bias b. Lastly, t causes a cell to die. he closest to a target position that is specified of a new connection is given by the parameter w. Two additional instructions (SETTAU and SETBIAS) spec- in ne c- ne ne
In this paper tion (DIVIDE) makes it possible for a mother cel daughter cell. A direction parameter (cv) and a dis ter (7) associated with that instruction specify the daughter cell to and one new afi current one is t we use a small set of general, low level, de- velopmental instructions (Table I). A cellular division instruc- to generate a tance parame- position of the be created in the coordinates of the local frame attached to the mother cell. Then, the local frame associated with the daughter cell is centered on that cell and is oriented the same way as the mother cell’s frame (Figure 1). Two instruc- tions (GROW and DRAW) respectively create one new efferent ferent connection. The cell to be linked to t by the instruction parameters (Figure 1). However, no conne tion is created i synaptic weight ify the values o instruction DIE f the target is outside the substrate’s limits. T f a cell’s time constant 7 and bias b. Lastly, t causes a cell to die. he closest to a target position that is specified of a new connection is given by the parameter w. Two additional instructions (SETTAU and SETBIAS) spec- in ne c- ne ne
We use an incremental approach that takes advantage of the zeometrical nature of the developmental model.
We use an incremental approach that takes advantage of the zeometrical nature of the developmental model.
ig. 5. Left: Each leg has two degrees of freedom. Thanks to the antagonis- tic PS- (Power Strike) and RS- (Return Strike) muscles, the leg can rotate around an axis (Ay) orthogonal to the plane of the figure. The UP- and DOWN-muscles allow the position of the foot F' to be translated along the (Az) axis. The resting lengths of the tunable PS-, RS- and UP-muscles depend on the activity levels of the corresponding motoneurons u. The rest- ing length of the DOWN-muscle is supposed to be non-tunable. Right: The muscle model. A muscle is modeled as a spring of set stiffness & and of (possibly tunable) resting length / (after [30]).
ig. 5. Left: Each leg has two degrees of freedom. Thanks to the antagonis- tic PS- (Power Strike) and RS- (Return Strike) muscles, the leg can rotate around an axis (Ay) orthogonal to the plane of the figure. The UP- and DOWN-muscles allow the position of the foot F' to be translated along the (Az) axis. The resting lengths of the tunable PS-, RS- and UP-muscles depend on the activity levels of the corresponding motoneurons u. The rest- ing length of the DOWN-muscle is supposed to be non-tunable. Right: The muscle model. A muscle is modeled as a spring of set stiffness & and of (possibly tunable) resting length / (after [30]).
x. 8. Left: The best subprogram 6 found in Experiment 2 (parameter values are not shown). This subprogram generates a tripod gait and is called LOCO! thereafter. Right: The corresponding artificial neural network after useless interneurons and connections have been pruned. Solid lines are excitatory connec tions, dotted lines are inhibitory connections. Fan-in connections arrive at the top and fan-out connections depart from the bottom of each neuron. The network contains 38 interneurons and 100 connections.
x. 8. Left: The best subprogram 6 found in Experiment 2 (parameter values are not shown). This subprogram generates a tripod gait and is called LOCO! thereafter. Right: The corresponding artificial neural network after useless interneurons and connections have been pruned. Solid lines are excitatory connec tions, dotted lines are inhibitory connections. Fan-in connections arrive at the top and fan-out connections depart from the bottom of each neuron. The network contains 38 interneurons and 100 connections.
cursor cell of Module 2. Such subprograms can create up to four neurons and 16 connections. Figure 15 depicts the initial setup for the developmental process.
cursor cell of Module 2. Such subprograms can create up to four neurons and 16 connections. Figure 15 depicts the initial setup for the developmental process.
Fig. 16. Behavior of a good individual in the three phases of the evaluation. That individual responds correctly to both stimuli. The conditional protocol (described in the text) prevents the animats from just predicting the time of occurence of the stimuli.
Fig. 16. Behavior of a good individual in the three phases of the evaluation. That individual responds correctly to both stimuli. The conditional protocol (described in the text) prevents the animats from just predicting the time of occurence of the stimuli.
Related topics:
GeneticsCognitive ScienceComputer ScienceDYNAMIC NEURAL NETWORK MODELSdynamic Neural Network
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